Semantic segmentation can be viewed as a related step towards MOS.
Recently, LiDAR-based semantic segmentation methods operating only on sensor data have achieved great success [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
However, most single LiDAR-frame semantic segmentation methods only find movable objects, e.g., vehicles and humans, but do not distinguish between actually moving objects, such as walking pedestrians or driving cars, and non-moving/static objects, like building structures or parked cars.
Wang et al.  [7]} also tackle the problem of segmenting things that could move from 3D laser scans of urban scenes, e.g., cars, pedestrians, and bicyclists.
Ruchti et al.  [8]} use a learning-based method to predict the probabilities of potentially movable objects.
Based on the semantic segmentation results [1]}, Chen et al.  [10]} propose a semantic LiDAR SLAM, which detects and filters out moving objects by checking the semantic consistency between online observation and semantic map representation.
